• Thursday, 20 December, morning
  • final exercise session (Mock Exam and visualisation exercises)
  • Feedback
  • Q&A
• Thursday, 20 December, afternoon
  • Decentral exam for exchange students!

• Example of scan in StudyNet
• Questions?

• Formatting data values for publication.
• Typical: String operations to make numbers and text look nicer.
  • Before creating a table or figure…

# load packages and data
library(tidyverse)
data("swiss")
# compute summary statistics
swiss_summary <-
  summarise(swiss, 
          avg_education = mean(Education, na.rm = TRUE),
          avg_fertility = mean(Fertility, na.rm = TRUE),
          N = n()
          )
swiss_summary

##   avg_education avg_fertility  N
## 1      10.97872      70.14255 47

Problems?

swiss_summary_rounded <- round(swiss_summary, 2)
swiss_summary_rounded

##   avg_education avg_fertility  N
## 1         10.98         70.14 47

• Coerce to text.
• String operations.
• Decimal marks, units (e.g., currencies), other special characters for special formats (e.g. coordinates).
• format()-function

swiss_form <- format(swiss_summary_rounded,
                     decimal.mark=",")
swiss_form 

##   avg_education avg_fertility  N
## 1         10,98         70,14 47

• Final step of data pipeline/data science procedure!
  • Convincingly communicating insights from data.
• R is a very powerful tool to do this!
  • (Very powerful graphics engine)

Three main approaches:

1. The original graphics package ((R Core Team 2018); shipped with the base R installation).

Three main approaches:

1. The original graphics package ((R Core Team 2018); shipped with the base R installation).
2. The lattice package (Sarkar 2008), an implementation of the original Bell Labs 'Trellis' system.

Three main approaches:

1. The original graphics package ((R Core Team 2018); shipped with the base R installation).
2. The lattice package (Sarkar 2008), an implementation of the original Bell Labs 'Trellis' system.
3. The ggplot2 package (Wickham 2016), an implementation of Leland Wilkinson's 'Grammar of Graphics'.

Using ggplot2 to generate a basic plot in R is quite simple. Basically, it involves three key points:

1. The data must be stored in a data.frame/tibble (in tidy format!).

Using ggplot2 to generate a basic plot in R is quite simple. Basically, it involves three key points:

1. The data must be stored in a data.frame/tibble (in tidy format!).
2. The starting point of a plot is always the function ggplot().

Using ggplot2 to generate a basic plot in R is quite simple. Basically, it involves three key points:

1. The data must be stored in a data.frame/tibble (in tidy format!).
2. The starting point of a plot is always the function ggplot().
3. The first line of plot code declares the data and the 'aesthetics' (e.g., which variables are mapped to the x-/y-axes):

Using ggplot2 to generate a basic plot in R is quite simple. Basically, it involves three key points:

1. The data must be stored in a data.frame/tibble (in tidy format!).
2. The starting point of a plot is always the function ggplot().
3. The first line of plot code declares the data and the 'aesthetics' (e.g., which variables are mapped to the x-/y-axes):

ggplot(data = my_dataframe, aes(x= xvar, y= yvar))

# load the R package
library(tidyverse) # automatically loads ggplot2
# load the data
data(swiss)
# get details about the data set
# ?swiss
# inspect the data
head(swiss)

##              Fertility Agriculture Examination Education Catholic Infant.Mortality
## Courtelary        80.2        17.0          15        12     9.96             22.2
## Delemont          83.1        45.1           6         9    84.84             22.2
## Franches-Mnt      92.5        39.7           5         5    93.40             20.2
## Moutier           85.8        36.5          12         7    33.77             20.3
## Neuveville        76.9        43.5          17        15     5.16             20.6
## Porrentruy        76.1        35.3           9         7    90.57             26.6

Code a province as 'Catholic' if more than 50% of the inhabitants are catholic:

# via tidyverse/mutate
swiss <- mutate(swiss, 
                Religion = 
                  ifelse(50 < Catholic, 'Catholic', 'Protestant'))

# 'old school' alternative
swiss$Religion <- 'Protestant'
swiss$Religion[50 < swiss$Catholic] <- 'Catholic'

# set to factor
swiss$Religion <- as.factor(swiss$Religion)

ggplot(data = swiss, aes(x = Education, y = Examination))

ggplot(data = swiss, aes(x = Education, y = Examination)) + 
     geom_point()

ggplot(data = swiss, aes(x = Education, y = Examination)) + 
     geom_point() +
     facet_wrap(~Religion)

ggplot(data = swiss, aes(x = Education, y = Examination)) + 
     geom_point() +
     geom_smooth(method = 'loess') +
     facet_wrap(~Religion)

ggplot(data = swiss, aes(x = Education, y = Examination)) + 
     geom_point() +
     geom_smooth(method = 'lm') +
     facet_wrap(~Religion)

ggplot(data = swiss, aes(x = Education, y = Examination)) + 
     geom_point(aes(color = Agriculture)) +
     geom_smooth(method = 'lm') +
     facet_wrap(~Religion)

ggplot(data = swiss, aes(x = Education, y = Examination)) + 
     geom_point(aes(color = Agriculture)) +
     geom_smooth(method = 'lm') +
     facet_wrap(~Religion) +
     coord_flip()

ggplot(data = swiss, aes(x = Education, y = Examination)) + 
     geom_point(aes(color = Agriculture)) +
     geom_smooth(method = 'lm') +
     facet_wrap(~Religion) +
     theme(legend.position = "bottom", axis.text=element_text(size=12) ) 

ggplot(data = swiss, aes(x = Education, y = Examination)) + 
     geom_point(aes(color = Agriculture)) +
     geom_smooth(method = 'lm') +
     facet_wrap(~Religion) +
     theme_minimal()

ggplot(data = swiss, aes(x = Education, y = Examination)) + 
     geom_point(aes(color = Agriculture)) +
     geom_smooth(method = 'lm') +
     facet_wrap(~Religion) +
     theme_dark()